• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.111-116
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• 2022

In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network(NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.

• Journal of Technologic Dentistry
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• v.22 no.1
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• pp.113-128
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• 2000

The purpose of this study was to serve as a basis for the development of dental technology and for creating a condition that dental technicians could work with pride and right values, by examining what problems there were in their health care, what they thought about them, and how the problems could be solved, The findings of this study were as below: 1. The most serious and harmful element in dental technology workshop was a dust(57.5%) and a noise(33.3%). 2. Approximately 99.0% of the dental technicians investigated made a complain of air pollution caused by noise. Their opinion on a possible measure to remove noise air pollution was that the noise-generating machine should be replaced(64.1%) or that it should be isolated(28.8%). 3. 76.0% complained air pollution cause by dust deteriorates their working efficiency. As a way to eliminate it, they suggested a dust chamber(35.3%) or an air cleaner(27.5%) should be installde. 4. About 80% made a complain of gas air pollution. The most common related symptom was a headache(56.9%). They thought that gas-generating machine should be isolated(39.9%) or that an air purifier should be prepared(33.3%). 5. The largest impact of heat and light on their body was weakening their eyesight(56.9%). 47.1% got burn once though four times, and 34.3% did five times or more. The way to prevent them was to install an automatic casting machine(66.0%) or use protective glasses(28.1%). 6. Approximately 47.7% were considering a change of occupation, and the most common reason was heavy work(23.5%), followed by poor prospect(21.6%) and working environment(19.0%) in the order named. 7. 88.9% responded they were likely to have an occupational disease. Their idea about the proper frequency of regular health examination was once a year(53.6%), or once per every six months(41.8%). 8. The field they were most interested in was health care(39.2%), followed by academic research activities(31.4%). This fact indicated it's most urgently required to improve their working environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.191-198
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• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• Journal of Technologic Dentistry
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• v.38 no.2
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• pp.63-68
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• 2016

Purpose: The purpose of this study was to assess the internal fitness of the PMMA 3-unit bridge that was fabricated with 5-axis milling machine and to verify the clinically allowable values. Methods: For fabrication of the crown bridge in this study, 25-27 abutment teeth were used. The prepare abutment teeth were scanned with a scanner and 3-unit bridge was designed by using design software. Upon the completion of the design, the 3-unit bridge was fabricated by using a PMMA block with 5-axis milling machine. The internal surface of the fabricated 3-unit bridge was scanned by using a scanner and the difference between the 3-unit bridge and the abutment teeth was assessed by merging them together. Results: $RMS{\pm}SD$ values for PRE group, MOL group, and BRI group were $51.2{\pm}18.2$, $44.8{\pm}10.0$, and $52.1{\pm}8.3{\mu}m$, respectively. The mean of the PRE group was bigger than that of the MOL and BRI group; however, statistically significant difference was not found (p>0.05). Conclusion: The PMMA 3-unit bridge that was fabricated with 5-axis milling machine presented stable internal values for each crown and overall internal values were within the range of clinically allowable values.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Journal of Korean Ophthalmic Optics Society
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• v.16 no.2
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• pp.107-115
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• 2011

Purpose: To design and fabricate the distributed auto edging machine for the development. Methods: We got the necessary data needed in design by using CAD. Based on the these data, we fabricated the trial product for the development of the distributed auto edging machine. Results: The patternless mode could be operated by receiving the eyesize data from the auto lay-outer with the RS232C transmission system and the pattern mode could be operated by setting the pattern on the left side of the machine. The distributed auto edging machine were composed with combinations of many elements; head, auto arm, pattern clamp and grinding wheels. The head part controlled the grinding of ophthalmic lens by operating the vertical and horizontal motors. The wheels part was comprised of glass mode, plastic mode, V-bevel mode and polish mode. The slide in the auto arm was equipped on the below of the patten and the slide could hold up the pattern which was rotated by fixed shaft. The pattern clamp could move the head part to the up and down or right or left way by the manual operation of optometrists. Conclusions: We could succeed in making the trial product by applying it to the development of the distributed auto edging machine which could be used as the patternless mode and pattern mode, selectively. Therefore, it was confidently expected that this product was very helpful for the optometrists to dispense the ophthalmic lens because of its cost-efficiency and convenience.